.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_revalidation/full_pipeline/_06_misorientation_full_pipeline.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_revalidation_full_pipeline__06_misorientation_full_pipeline.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_revalidation_full_pipeline__06_misorientation_full_pipeline.py:


.. _pipeline_val_misorientation_results:

Full-pipeline performance under simulated mounting errors
=========================================================

This analysis compares the free-living full-pipeline validation results across
simulated lower-back sensor mounting orientations. It uses the result files
generated by :ref:`pipeline_val_misorientation_gen`.

The comparison contains the default full pipeline without per-GS reorientation
and a full-mode reorientation variant where both cohort-specific sub-pipelines
use :class:`~mobgap.gait_sequences.GsdIonescu`.

The default regular-walking GSD, :class:`~mobgap.gait_sequences.GsdIluz`, is
therefore present in the default pipeline only. It is not evaluated with
reorientation enabled because ``GsdIluz`` requires body-frame input, while
per-GS reorientation requires gait sequence detection to work on the unknown
sensor frame before correction.

The reorientation-enabled variant intentionally uses ``correction_mode="full"``
instead of the default ``trust_gravity`` mode. This analysis creates one result
for every simulated orientation class per recording, i.e. an equal-prevalence
stress test. This makes the specific front-back flip class that
``trust_gravity`` intentionally does not correct much more common than expected
in a realistic low-error setting. Under this validation setup, ``full`` is the
appropriate correction mode. For realistic expected orientation-error
prevalence, ``trust_gravity`` can outperform ``full``; see the dedicated
reorientation validation analysis for that trade-off.

The analysis remains split into regular-walking and impaired-walking cohort
groups because the official full-pipeline sub-pipelines differ downstream.

.. GENERATED FROM PYTHON SOURCE LINES 37-39

Compared Pipelines
------------------

.. GENERATED FROM PYTHON SOURCE LINES 39-71

.. code-block:: Python

    from pathlib import Path

    import matplotlib.pyplot as plt
    import pandas as pd
    import seaborn as sns
    from mobgap.data.validation_results import ValidationResultLoader
    from mobgap.plotting import (
        calc_min_max_with_margin,
        make_square,
        move_legend_outside,
    )
    from mobgap.re_orientation.pipeline import REORIENTATION_LABELS
    from mobgap.utils.misc import get_env_var

    algorithms = {
        "Official_MobiliseD_Pipeline": "Default pipeline",
        "Official_MobiliseD_Pipeline__gsd_ionescu_reorientation": (
            "GsdIonescu + full reorientation"
        ),
    }
    algorithm_order = list(algorithms.values())
    orientation_order = list(REORIENTATION_LABELS)

    regular_walking_cohorts = ["HA", "COPD", "CHF"]
    impaired_walking_cohorts = ["MS", "PD", "PFF"]

    dmos = {
        "walking_speed_mps": ("Walking speed", "m/s"),
        "stride_length_m": ("Stride length", "m"),
        "cadence_spm": ("Cadence", "steps/min"),
    }








.. GENERATED FROM PYTHON SOURCE LINES 72-79

Why Full Reorientation Mode Here?
---------------------------------
The full-pipeline misorientation validation is not meant to model realistic
mounting-error prevalence. Instead, it deliberately creates an equal number of
datasets for the identity orientation and for every simulated misorientation.
With eight simulated orientation classes, the validation therefore corresponds
to the prevalence example below.

.. GENERATED FROM PYTHON SOURCE LINES 79-102

.. code-block:: Python

    equal_prevalence_example = pd.DataFrame(
        {
            "Prevalence in this validation": [
                1 / len(orientation_order),
                (len(orientation_order) - 1) / len(orientation_order),
                1 / len(orientation_order),
            ],
            "Interpretation": [
                "correctly oriented recordings",
                "recordings with any simulated misorientation",
                "front-back flip class ignored by trust_gravity",
            ],
        },
        index=[
            "Identity orientation",
            "Any misorientation",
            "trust_gravity-ignored PA flip",
        ],
    )
    equal_prevalence_example.style.format(
        {"Prevalence in this validation": "{:.1%}"}
    )






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style type="text/css">
    </style>
    <table id="T_23c44">
      <thead>
        <tr>
          <th class="blank level0" >&nbsp;</th>
          <th id="T_23c44_level0_col0" class="col_heading level0 col0" >Prevalence in this validation</th>
          <th id="T_23c44_level0_col1" class="col_heading level0 col1" >Interpretation</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th id="T_23c44_level0_row0" class="row_heading level0 row0" >Identity orientation</th>
          <td id="T_23c44_row0_col0" class="data row0 col0" >12.5%</td>
          <td id="T_23c44_row0_col1" class="data row0 col1" >correctly oriented recordings</td>
        </tr>
        <tr>
          <th id="T_23c44_level0_row1" class="row_heading level0 row1" >Any misorientation</th>
          <td id="T_23c44_row1_col0" class="data row1 col0" >87.5%</td>
          <td id="T_23c44_row1_col1" class="data row1 col1" >recordings with any simulated misorientation</td>
        </tr>
        <tr>
          <th id="T_23c44_level0_row2" class="row_heading level0 row2" >trust_gravity-ignored PA flip</th>
          <td id="T_23c44_row2_col0" class="data row2 col0" >12.5%</td>
          <td id="T_23c44_row2_col1" class="data row2 col1" >front-back flip class ignored by trust_gravity</td>
        </tr>
      </tbody>
    </table>

    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 103-109

Under this equal-prevalence stress test, the PA flip class that
``trust_gravity`` intentionally skips is frequent enough that the ``full``
mode is the more informative validation target. In realistic studies, the
expected misorientation rate is usually much lower; in that regime,
``trust_gravity`` can outperform ``full`` because it avoids unnecessary
data-driven front-back corrections for correctly oriented gait sequences.

.. GENERATED FROM PYTHON SOURCE LINES 111-116

Loading The Free-Living Results
-------------------------------
The result files add ``orientation`` to the usual TVS index. The standard
validation loader keeps unknown CSV columns as data columns, so we can still
use it for local and remote loading.

.. GENERATED FROM PYTHON SOURCE LINES 116-171

.. code-block:: Python

    local_data_path = (
        Path(get_env_var("MOBGAP_VALIDATION_DATA_PATH")) / "results"
        if int(get_env_var("MOBGAP_VALIDATION_USE_LOCAL_DATA", 0))
        else None
    )
    __RESULT_VERSION = "main"
    loader = ValidationResultLoader(
        "full_pipeline_misorientation",
        result_path=local_data_path,
        version=__RESULT_VERSION,
    )


    def load_misorientation_results() -> pd.DataFrame:
        results = []
        for folder_name, algorithm_label in algorithms.items():
            data = (
                loader.load_single_results(folder_name, "free_living")
                .reset_index()
                .assign(algorithm=algorithm_label)
            )
            results.append(data)
        return pd.concat(results, ignore_index=True)


    def combined_error_long(
        data: pd.DataFrame, *, value_suffix: str
    ) -> pd.DataFrame:
        results = []
        id_vars = [
            "algorithm",
            "orientation",
            "cohort",
            "participant_id",
            "time_measure",
            "recording",
            "recording_name",
            "recording_name_pretty",
        ]
        for dmo, (dmo_label, unit) in dmos.items():
            column = f"combined__{dmo}__{value_suffix}"
            results.append(
                data[[*id_vars, column]]
                .rename(columns={column: "error"})
                .assign(dmo=dmo_label, unit=unit)
            )
        return pd.concat(results, ignore_index=True)


    free_living_results = load_misorientation_results()
    combined_errors = combined_error_long(free_living_results, value_suffix="error")
    combined_abs_errors = combined_error_long(
        free_living_results, value_suffix="abs_error"
    )





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


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.. GENERATED FROM PYTHON SOURCE LINES 172-174

Plot Helpers
------------

.. GENERATED FROM PYTHON SOURCE LINES 174-331

.. code-block:: Python

    sns.set_context("talk")


    def plot_combined_error_boxplots(
        data: pd.DataFrame, *, title: str, ylabel_prefix: str
    ) -> None:
        fig, axes = plt.subplots(
            1, 3, figsize=(20, 7), sharex=True, constrained_layout=True
        )
        for ax, (dmo_label, unit) in zip(axes, dmos.values()):
            plot_data = data[data["dmo"] == dmo_label]
            sns.boxplot(
                data=plot_data,
                x="algorithm",
                y="error",
                hue="orientation",
                order=algorithm_order,
                hue_order=orientation_order,
                showmeans=True,
                ax=ax,
            )
            ax.axhline(0, color="0.4", linewidth=1, linestyle=":", zorder=-50)
            ax.set_title(dmo_label)
            ax.set_xlabel("")
            ax.set_ylabel(f"{ylabel_prefix} [{unit}]")
            ax.tick_params(axis="x", rotation=20)
            ax.grid(True, axis="y", alpha=0.3)
            ax.legend(title="Orientation")

        fig.suptitle(title)
        move_legend_outside(fig, axes[-1], ncol=4)
        plt.show()


    def matched_wb_count_table(data: pd.DataFrame) -> pd.DataFrame:
        matched_wb_counts = data.assign(
            n_matched_wbs=data["matched__n_matched_wbs"].fillna(0).astype(int)
        )
        table = (
            matched_wb_counts.pivot_table(
                index="algorithm",
                columns="orientation",
                values="n_matched_wbs",
                aggfunc="sum",
                sort=False,
            )
            .reindex(index=algorithm_order, columns=orientation_order)
            .fillna(0)
            .astype(int)
        )
        return table.rename_axis(index="Algorithm", columns="Orientation")


    def plot_matched_wb_counts(data: pd.DataFrame, *, title: str) -> None:
        plot_data = (
            data.assign(
                n_matched_wbs=data["matched__n_matched_wbs"].fillna(0).astype(int)
            )
            .groupby(["algorithm", "orientation"], sort=False)["n_matched_wbs"]
            .sum()
            .reset_index()
        )
        fig, ax = plt.subplots(figsize=(16, 7), constrained_layout=True)
        sns.barplot(
            data=plot_data,
            x="orientation",
            y="n_matched_wbs",
            hue="algorithm",
            order=orientation_order,
            hue_order=algorithm_order,
            ax=ax,
        )
        ax.set_title(title)
        ax.set_xlabel("Orientation")
        ax.set_ylabel("# matched WBs")
        ax.tick_params(axis="x", rotation=25)
        ax.grid(True, axis="y", alpha=0.3)
        move_legend_outside(fig, ax, ncol=2)
        plt.show()


    def identity_walking_speed_error_comparison(data: pd.DataFrame) -> pd.DataFrame:
        id_vars = [
            "cohort",
            "participant_id",
            "time_measure",
            "recording",
            "recording_name",
            "recording_name_pretty",
        ]
        error_col = "combined__walking_speed_mps__error"
        identity_data = data[data["orientation"] == "identity"]
        baseline_errors = (
            identity_data[identity_data["algorithm"] == "Default pipeline"][
                [*id_vars, error_col]
            ]
            .rename(columns={error_col: "baseline_error"})
            .copy()
        )
        reorientation_errors = (
            identity_data[
                identity_data["algorithm"] == "GsdIonescu + full reorientation"
            ][[*id_vars, error_col]]
            .rename(columns={error_col: "reorientation_error"})
            .copy()
        )
        return baseline_errors.merge(reorientation_errors, on=id_vars, how="inner")


    def plot_identity_walking_speed_error_correlation(data: pd.DataFrame) -> None:
        comparison = identity_walking_speed_error_comparison(data)
        cohort_groups = [
            ("Regular-walking cohorts", regular_walking_cohorts),
            ("Impaired-walking cohorts", impaired_walking_cohorts),
        ]
        fig, axes = plt.subplots(1, 2, figsize=(14, 7), constrained_layout=True)
        legend_handles = {}
        for ax, (title, cohorts) in zip(axes, cohort_groups):
            plot_data = comparison[comparison["cohort"].isin(cohorts)].dropna(
                subset=["baseline_error", "reorientation_error"]
            )
            sns.scatterplot(
                data=plot_data,
                x="baseline_error",
                y="reorientation_error",
                hue="cohort",
                ax=ax,
                s=80,
            )
            min_max = calc_min_max_with_margin(
                plot_data["baseline_error"],
                plot_data["reorientation_error"],
            )
            make_square(ax, min_max)
            ax.set_title(title)
            ax.set_xlabel("Default-pipeline identity WS error [m/s]")
            ax.set_ylabel("Full-reorientation identity WS error [m/s]")
            ax.grid(True, alpha=0.3)
            handles, labels = ax.get_legend_handles_labels()
            legend_handles.update(dict(zip(labels, handles)))
            ax.get_legend().remove()

        fig.suptitle("Identity-orientation combined walking-speed errors")
        fig.legend(
            legend_handles.values(),
            legend_handles.keys(),
            loc="outside lower center",
            ncol=3,
            frameon=False,
        )
        plt.show()


    def cohort_group_data(data: pd.DataFrame, cohorts: list[str]) -> pd.DataFrame:
        return data[data["cohort"].isin(cohorts)]









.. GENERATED FROM PYTHON SOURCE LINES 332-337

Matched Walking Bout Counts
---------------------------
These tables and plots show the total number of matched walking bouts per
pipeline variant and simulated orientation. They are split by cohort group
because the official healthy and impaired sub-pipelines differ downstream.

.. GENERATED FROM PYTHON SOURCE LINES 337-342

.. code-block:: Python

    matched_wb_counts_regular = matched_wb_count_table(
        cohort_group_data(free_living_results, regular_walking_cohorts)
    )
    matched_wb_counts_regular  # noqa: B018






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th>Orientation</th>
          <th>identity</th>
          <th>pa_normal__rot_pa_pos90</th>
          <th>pa_normal__rot_pa_180</th>
          <th>pa_normal__rot_pa_neg90</th>
          <th>pa_flipped__rot_pa_0</th>
          <th>pa_flipped__rot_pa_pos90</th>
          <th>pa_flipped__rot_pa_180</th>
          <th>pa_flipped__rot_pa_neg90</th>
        </tr>
        <tr>
          <th>Algorithm</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>Default pipeline</th>
          <td>1154</td>
          <td>0</td>
          <td>0</td>
          <td>0</td>
          <td>1170</td>
          <td>0</td>
          <td>0</td>
          <td>0</td>
        </tr>
        <tr>
          <th>GsdIonescu + full reorientation</th>
          <td>1228</td>
          <td>1228</td>
          <td>1227</td>
          <td>1228</td>
          <td>1228</td>
          <td>1228</td>
          <td>1227</td>
          <td>1228</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 343-348

.. code-block:: Python

    matched_wb_counts_impaired = matched_wb_count_table(
        cohort_group_data(free_living_results, impaired_walking_cohorts)
    )
    matched_wb_counts_impaired  # noqa: B018






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th>Orientation</th>
          <th>identity</th>
          <th>pa_normal__rot_pa_pos90</th>
          <th>pa_normal__rot_pa_180</th>
          <th>pa_normal__rot_pa_neg90</th>
          <th>pa_flipped__rot_pa_0</th>
          <th>pa_flipped__rot_pa_pos90</th>
          <th>pa_flipped__rot_pa_180</th>
          <th>pa_flipped__rot_pa_neg90</th>
        </tr>
        <tr>
          <th>Algorithm</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>Default pipeline</th>
          <td>830</td>
          <td>625</td>
          <td>815</td>
          <td>657</td>
          <td>830</td>
          <td>647</td>
          <td>810</td>
          <td>634</td>
        </tr>
        <tr>
          <th>GsdIonescu + full reorientation</th>
          <td>830</td>
          <td>830</td>
          <td>830</td>
          <td>830</td>
          <td>831</td>
          <td>831</td>
          <td>831</td>
          <td>831</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 349-354

.. code-block:: Python

    plot_matched_wb_counts(
        cohort_group_data(free_living_results, regular_walking_cohorts),
        title="Matched WBs in regular-walking cohorts",
    )




.. image-sg:: /auto_revalidation/full_pipeline/images/sphx_glr__06_misorientation_full_pipeline_001.png
   :alt: Matched WBs in regular-walking cohorts
   :srcset: /auto_revalidation/full_pipeline/images/sphx_glr__06_misorientation_full_pipeline_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 355-360

.. code-block:: Python

    plot_matched_wb_counts(
        cohort_group_data(free_living_results, impaired_walking_cohorts),
        title="Matched WBs in impaired-walking cohorts",
    )




.. image-sg:: /auto_revalidation/full_pipeline/images/sphx_glr__06_misorientation_full_pipeline_002.png
   :alt: Matched WBs in impaired-walking cohorts
   :srcset: /auto_revalidation/full_pipeline/images/sphx_glr__06_misorientation_full_pipeline_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 361-365

Identity-Orientation Walking-Speed Error Correlation
----------------------------------------------------
This plot checks whether enabling full-mode reorientation changes walking-speed
performance when the simulated mounting is already correct.

.. GENERATED FROM PYTHON SOURCE LINES 365-367

.. code-block:: Python

    plot_identity_walking_speed_error_correlation(free_living_results)




.. image-sg:: /auto_revalidation/full_pipeline/images/sphx_glr__06_misorientation_full_pipeline_003.png
   :alt: Identity-orientation combined walking-speed errors, Regular-walking cohorts, Impaired-walking cohorts
   :srcset: /auto_revalidation/full_pipeline/images/sphx_glr__06_misorientation_full_pipeline_003.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 368-373

Regular-Walking Cohorts
-----------------------
``GsdIluz`` is the default GSD for these cohorts. It remains part of the
default-pipeline baseline, but the full-mode reorientation variant uses
``GsdIonescu`` because ``GsdIluz`` cannot run before per-GS reorientation.

.. GENERATED FROM PYTHON SOURCE LINES 373-379

.. code-block:: Python

    plot_combined_error_boxplots(
        cohort_group_data(combined_errors, regular_walking_cohorts),
        title="Combined DMO errors in regular-walking cohorts",
        ylabel_prefix="Combined error",
    )




.. image-sg:: /auto_revalidation/full_pipeline/images/sphx_glr__06_misorientation_full_pipeline_004.png
   :alt: Combined DMO errors in regular-walking cohorts, Walking speed, Stride length, Cadence
   :srcset: /auto_revalidation/full_pipeline/images/sphx_glr__06_misorientation_full_pipeline_004.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 380-386

.. code-block:: Python

    plot_combined_error_boxplots(
        cohort_group_data(combined_abs_errors, regular_walking_cohorts),
        title="Combined absolute DMO errors in regular-walking cohorts",
        ylabel_prefix="Combined absolute error",
    )




.. image-sg:: /auto_revalidation/full_pipeline/images/sphx_glr__06_misorientation_full_pipeline_005.png
   :alt: Combined absolute DMO errors in regular-walking cohorts, Walking speed, Stride length, Cadence
   :srcset: /auto_revalidation/full_pipeline/images/sphx_glr__06_misorientation_full_pipeline_005.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 387-391

Impaired-Walking Cohorts
------------------------
These cohorts already use ``GsdIonescu`` in the default pipeline, so the main
change for the modified variant is enabling full-mode per-GS reorientation.

.. GENERATED FROM PYTHON SOURCE LINES 391-397

.. code-block:: Python

    plot_combined_error_boxplots(
        cohort_group_data(combined_errors, impaired_walking_cohorts),
        title="Combined DMO errors in impaired-walking cohorts",
        ylabel_prefix="Combined error",
    )




.. image-sg:: /auto_revalidation/full_pipeline/images/sphx_glr__06_misorientation_full_pipeline_006.png
   :alt: Combined DMO errors in impaired-walking cohorts, Walking speed, Stride length, Cadence
   :srcset: /auto_revalidation/full_pipeline/images/sphx_glr__06_misorientation_full_pipeline_006.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 398-403

.. code-block:: Python

    plot_combined_error_boxplots(
        cohort_group_data(combined_abs_errors, impaired_walking_cohorts),
        title="Combined absolute DMO errors in impaired-walking cohorts",
        ylabel_prefix="Combined absolute error",
    )



.. image-sg:: /auto_revalidation/full_pipeline/images/sphx_glr__06_misorientation_full_pipeline_007.png
   :alt: Combined absolute DMO errors in impaired-walking cohorts, Walking speed, Stride length, Cadence
   :srcset: /auto_revalidation/full_pipeline/images/sphx_glr__06_misorientation_full_pipeline_007.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 8.270 seconds)

**Estimated memory usage:**  82 MB


.. _sphx_glr_download_auto_revalidation_full_pipeline__06_misorientation_full_pipeline.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: _06_misorientation_full_pipeline.ipynb <_06_misorientation_full_pipeline.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: _06_misorientation_full_pipeline.py <_06_misorientation_full_pipeline.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: _06_misorientation_full_pipeline.zip <_06_misorientation_full_pipeline.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_